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@Article{CostaSalasMaBeOlChLo:2015:ClSeVa,
               author = "Costa Salas, Yasel L. and Mart{\'{\i}}nez P{\'e}rez, Carlos A. 
                         and Bello, Rafael and Oliveira, Alexandre C. and Chaves, Antonio 
                         A. and Lorena, Luiz Antonio Nogueira",
          affiliation = "{Universidad de Manizales} and {Universidad Central “Marta Abreu” 
                         de Las Villas} and {Universidad Central “Marta Abreu” de Las 
                         Villas} and {Universidade Federal do Maranh{\~a}o (UFMA)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Clustering search and variable mesh algorithms for continuous 
                         optimization",
              journal = "Expert Systems with Applications",
                 year = "2015",
               volume = "42",
               number = "2",
                pages = "789--795",
                month = "Feb.",
             keywords = "Continuous function optimization, Hybrid methods.",
             abstract = "The hybridization of population-based meta-heuristics and local 
                         search strategies is an effective algorithmic proposal for solving 
                         complex continuous optimization problems. Such hybridization 
                         becomes much more effective when the local search heuristics are 
                         applied in the most promising areas of the solution space. This 
                         paper presents a hybrid method based on Clustering Search (CS) to 
                         solve continuous optimization problems. The CS divides the search 
                         space in clusters, which are composed of solutions generated by a 
                         population meta-heuristic, called Variable Mesh Optimization. Each 
                         cluster is explored further with local search procedures. 
                         Computational results considering a benchmark of multimodal 
                         continuous functions are presented.",
                  doi = "10.1016/j.eswa.2014.08.040",
                  url = "http://dx.doi.org/10.1016/j.eswa.2014.08.040",
                 issn = "0957-4174",
             language = "en",
        urlaccessdate = "29 nov. 2020"
}


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